// Start training from the collected faces.
// The face recognition algorithm can be one of these and perhaps more, depending on your version of OpenCV, which must be atleast v2.4.1:
// "FaceRecognizer.Eigenfaces": Eigenfaces, also referred to as PCA (Turk and Pentland, 1991).
// "FaceRecognizer.Fisherfaces": Fisherfaces, also referred to as LDA (Belhumeur et al, 1997).
// "FaceRecognizer.LBPH": Local Binary Pattern Histograms (Ahonen et al, 2006).
Ptr<FaceRecognizer> learnCollectedFaces(const vector<Mat> preprocessedFaces, const vector<int> faceLabels, const string facerecAlgorithm)
{
Ptr<FaceRecognizer> model;
cout << "Learning the collected faces using the [" << facerecAlgorithm << "] algorithm ..." << endl;
// Make sure the "contrib" module is dynamically loaded at runtime.
// Requires OpenCV v2.4.1 or later (from June 2012), otherwise the FaceRecognizer will not compile or run!
bool haveContribModule = initModule_contrib();
if (!haveContribModule) {
cerr << "ERROR: The 'contrib' module is needed for FaceRecognizer but has not been loaded into OpenCV!" << endl;
exit(1);
}
// Use the new FaceRecognizer class in OpenCV's "contrib" module:
// Requires OpenCV v2.4.1 or later (from June 2012), otherwise the FaceRecognizer will not compile or run!
model = Algorithm::create<FaceRecognizer>(facerecAlgorithm);
if (model.empty()) {
cerr << "ERROR: The FaceRecognizer algorithm [" << facerecAlgorithm << "] is not available in your version of OpenCV. Please update to OpenCV v2.4.1 or newer." << endl;
exit(1);
}
// Do the actual training from the collected faces. Might take several seconds or minutes depending on input!
model->train(preprocessedFaces, faceLabels);
return model;
}
// Start training from the collected faces.
// The face recognition algorithm can be one of these and perhaps more, depending on your version of OpenCV, which must be atleast v2.4.1:
// "FaceRecognizer.Eigenfaces": Eigenfaces, also referred to as PCA (Turk and Pentland, 1991).
// "FaceRecognizer.Fisherfaces": Fisherfaces, also referred to as LDA (Belhumeur et al, 1997).
// "FaceRecognizer.LBPH": Local Binary Pattern Histograms (Ahonen et al, 2006).
//Ptr<FaceRecognizer> learnCollectedFaces(const vector<Mat> preprocessedFaces, const vector<int> faceLabels, const string facerecAlgorithm)
void *learnCollectedFaces(void *arg)
{
Ptr<FaceRecognizer> model;
while (true)
{
//std::unique_lock<std::mutex> lock(trainingSignalMutex);
//waitForTrainingSignal.wait(lock, [] {return 1;});
pthread_mutex_lock(&ptrainingSignalMutex);
pthread_cond_wait(&pwaitForTrainingSignal, &ptrainingSignalMutex);
cout << "Learning the collected faces using the [" << facerecAlgorithm << "] algorithm ..." << endl;
// Make sure the "contrib" module is dynamically loaded at runtime.
// Requires OpenCV v2.4.1 or later (from June 2012), otherwise the FaceRecognizer will not compile or run!
bool haveContribModule = initModule_contrib();
if (!haveContribModule) {
cerr << "ERROR: The 'contrib' module is needed for FaceRecognizer but has not been loaded into OpenCV!" << endl;
exit(1);
}
// Use the new FaceRecognizer class in OpenCV's "contrib" module:
// Requires OpenCV v2.4.1 or later (from June 2012), otherwise the FaceRecognizer will not compile or run!
model = Algorithm::create<FaceRecognizer>(facerecAlgorithm);
if (model.empty()) {
cerr << "ERROR: The FaceRecognizer algorithm [" << facerecAlgorithm << "] is not available in your version of OpenCV. Please update to OpenCV v2.4.1 or newer." << endl;
exit(1);
}
trackTrainingCompletion = 1;
// Do the actual training from the collected faces. Might take several seconds or minutes depending on input!
model->train(preprocessedFaces, faceLabels);
if (canUseFaceRecognitionModel == false)
{
// This variable prevents use of face recognizer model, if training has not been done even once
canUseFaceRecognitionModel = true;
}
trackTrainingCompletion = 2;
pthread_mutex_unlock(&ptrainingSignalMutex);
//return model;
//std::lock_guard<std::mutex> guard(modelUpdationMutex);
pthread_mutex_lock(&pmodelUpdationMutex);
faceRecognitionModel = model;
pthread_mutex_unlock(&pmodelUpdationMutex);
}
}
